no code implementations • ECCV 2020 • Shen Sang, Manmohan Chandraker
We present a novel physically-motivated deep network for joint shape and material estimation, as well as relighting under novel illumination conditions, using a single image captured by a mobile phone camera.
no code implementations • 5 May 2024 • Di Liu, Bingbing Zhuang, Dimitris N. Metaxas, Manmohan Chandraker
Specifically, due to the lack of correspondences between consecutive frames of sparse Lidar point clouds, static objects might appear to be moving - the so-called swimming effect.
no code implementations • 1 May 2024 • Shanlin Sun, Bingbing Zhuang, Ziyu Jiang, Buyu Liu, Xiaohui Xie, Manmohan Chandraker
In this paper, we propose several insights that allow a better utilization of Lidar data to improve NeRF quality on street scenes.
no code implementations • 23 Apr 2024 • Manyi Yao, Abhishek Aich, Yumin Suh, Amit Roy-Chowdhury, Christian Shelton, Manmohan Chandraker
The third step is to use the aforementioned derived dataset to train a gating network that predicts the number of encoder layers to be used, conditioned on the input image.
no code implementations • 23 Apr 2024 • Abhishek Aich, Yumin Suh, Samuel Schulter, Manmohan Chandraker
With efficiency being a high priority for scaling such models, we observed that the state-of-the-art method Mask2Former uses ~50% of its compute only on the transformer encoder.
no code implementations • 6 Apr 2024 • Zaid Khan, Vijay Kumar BG, Samuel Schulter, Yun Fu, Manmohan Chandraker
We propose a method where we exploit existing annotations for a vision-language task to improvise a coarse reward signal for that task, treat the LLM as a policy, and apply reinforced self-training to improve the visual program synthesis ability of the LLM for that task.
no code implementations • 26 Mar 2024 • Mingfu Liang, Jong-Chyi Su, Samuel Schulter, Sparsh Garg, Shiyu Zhao, Ying Wu, Manmohan Chandraker
This necessitates an expensive process of continuously curating and annotating data with significant human effort.
no code implementations • 8 Mar 2024 • Tarun Kalluri, Bodhisattwa Prasad Majumder, Manmohan Chandraker
We introduce LaGTran, a novel framework that utilizes readily available or easily acquired text descriptions to guide robust transfer of discriminative knowledge from labeled source to unlabeled target data with domain shifts.
no code implementations • 17 Jan 2024 • Yu-Ying Yeh, Jia-Bin Huang, Changil Kim, Lei Xiao, Thu Nguyen-Phuoc, Numair Khan, Cheng Zhang, Manmohan Chandraker, Carl S Marshall, Zhao Dong, Zhengqin Li
In contrast, TextureDreamer can transfer highly detailed, intricate textures from real-world environments to arbitrary objects with only a few casually captured images, potentially significantly democratizing texture creation.
no code implementations • 4 Jan 2024 • Alex Trevithick, Matthew Chan, Towaki Takikawa, Umar Iqbal, Shalini De Mello, Manmohan Chandraker, Ravi Ramamoorthi, Koki Nagano
3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of 2D images via neural volume rendering.
no code implementations • 31 Dec 2023 • Wei-Jer Chang, Francesco Pittaluga, Masayoshi Tomizuka, Wei Zhan, Manmohan Chandraker
These findings affirm that guided diffusion models provide a robust and versatile foundation for safety-critical, interactive traffic simulation, extending their utility across the broader landscape of autonomous driving.
no code implementations • 30 Dec 2023 • S P Sharan, Francesco Pittaluga, Vijay Kumar B G, Manmohan Chandraker
Although planning is a crucial component of the autonomous driving stack, researchers have yet to develop robust planning algorithms that are capable of safely handling the diverse range of possible driving scenarios.
1 code implementation • 29 Dec 2023 • Shiyu Zhao, Long Zhao, Vijay Kumar B. G, Yumin Suh, Dimitris N. Metaxas, Manmohan Chandraker, Samuel Schulter
The recent progress in language-based open-vocabulary object detection can be largely attributed to finding better ways of leveraging large-scale data with free-form text annotations.
no code implementations • 2 Dec 2023 • Salman S. Khan, Xiang Yu, Kaushik Mitra, Manmohan Chandraker, Francesco Pittaluga
OpEnCam encrypts the incoming light before capturing it using the modulating ability of optical masks.
no code implementations • ICCV 2023 • Mateusz Michalkiewicz, Masoud Faraki, Xiang Yu, Manmohan Chandraker, Mahsa Baktashmotlagh
Overfitting to the source domain is a common issue in gradient-based training of deep neural networks.
no code implementations • ICCV 2023 • Abhishek Aich, Samuel Schulter, Amit K. Roy-Chowdhury, Manmohan Chandraker, Yumin Suh
Further, we present a simple but effective search algorithm that translates user constraints to runtime width configurations of both the shared encoder and task decoders, for sampling the sub-architectures.
no code implementations • ICCV 2023 • Ishit Mehta, Manmohan Chandraker, Ravi Ramamoorthi
We introduce a theoretical framework for differentiable surface evolution that allows discrete topology changes through the use of topological derivatives for variational optimization of image functionals.
2 code implementations • 11 Aug 2023 • Shiyu Zhao, Samuel Schulter, Long Zhao, Zhixing Zhang, Vijay Kumar B. G, Yumin Suh, Manmohan Chandraker, Dimitris N. Metaxas
This work identifies two challenges of using self-training in OVD: noisy PLs from VLMs and frequent distribution changes of PLs.
1 code implementation • CVPR 2023 • Zaid Khan, Vijay Kumar BG, Samuel Schulter, Xiang Yu, Yun Fu, Manmohan Chandraker
We introduce SelTDA (Self-Taught Data Augmentation), a strategy for finetuning large VLMs on small-scale VQA datasets.
no code implementations • CVPR 2023 • Zhixiang Min, Bingbing Zhuang, Samuel Schulter, Buyu Liu, Enrique Dunn, Manmohan Chandraker
Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature.
no code implementations • 18 May 2023 • Chaitanya Animesh, Manmohan Chandraker
A recent state-of-the-art, supervised contrastive (SupCon) loss, extends self-supervised contrastive learning to supervised setting by generalizing to multiple positives and negatives in a batch and improves upon the cross-entropy loss.
no code implementations • 7 May 2023 • Zhengqin Li, Li Yu, Mikhail Okunev, Manmohan Chandraker, Zhao Dong
For training, we significantly enhance the OpenRooms public dataset of photorealistic synthetic indoor scenes with around 360K HDR environment maps of much higher resolution and 38K video sequences, rendered with GPU-based path tracing.
no code implementations • 3 May 2023 • Alex Trevithick, Matthew Chan, Michael Stengel, Eric R. Chan, Chao Liu, Zhiding Yu, Sameh Khamis, Manmohan Chandraker, Ravi Ramamoorthi, Koki Nagano
We present a one-shot method to infer and render a photorealistic 3D representation from a single unposed image (e. g., face portrait) in real-time.
1 code implementation • ICCV 2023 • Liwen Wu, Rui Zhu, Mustafa B. Yaldiz, Yinhao Zhu, Hong Cai, Janarbek Matai, Fatih Porikli, Tzu-Mao Li, Manmohan Chandraker, Ravi Ramamoorthi
Inverse path tracing has recently been applied to joint material and lighting estimation, given geometry and multi-view HDR observations of an indoor scene.
no code implementations • CVPR 2023 • Tarun Kalluri, Wangdong Xu, Manmohan Chandraker
In recent years, several efforts have been aimed at improving the robustness of vision models to domains and environments unseen during training.
no code implementations • 9 Mar 2023 • Tarun Kalluri, Weiyao Wang, Heng Wang, Manmohan Chandraker, Lorenzo Torresani, Du Tran
Many top-down architectures for instance segmentation achieve significant success when trained and tested on pre-defined closed-world taxonomy.
no code implementations • European Conference on Computer Vision (ECCV) 2022 • Zaid Tasneem, Giovanni Milione, Yi-Hsuan Tsai, Xiang Yu, Ashok Veeraraghavan, Manmohan Chandraker, Francesco Pittaluga
With over a billion sold each year, cameras are not only becoming ubiquitous and omnipresent, but are driving progress in a wide range of applications such as augmented/virtual reality, robotics, surveillance, security, autonomous navigation and many others.
no code implementations • 28 Oct 2022 • Sriram Narayanan, Dinesh Jayaraman, Manmohan Chandraker
We address key challenges in long-horizon embodied exploration and navigation by proposing a new object transport task and a novel modular framework for temporally extended navigation.
no code implementations • 23 Oct 2022 • Shubham Dokania, A. H. Abdul Hafez, Anbumani Subramanian, Manmohan Chandraker, C. V. Jawahar
Autonomous driving and assistance systems rely on annotated data from traffic and road scenarios to model and learn the various object relations in complex real-world scenarios.
1 code implementation • 16 Aug 2022 • Shubham Dokania, Anbumani Subramanian, Manmohan Chandraker, C. V. Jawahar
We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation, mimicking real scene properties with high-fidelity, along with mechanisms to diversify samples in a physically meaningful way.
no code implementations • 4 Aug 2022 • Tarun Kalluri, Manmohan Chandraker
Domain adaptation for semantic segmentation across datasets consisting of the same categories has seen several recent successes.
1 code implementation • 27 Jul 2022 • Zhanpeng Feng, Shiliang Zhang, Rinyoichi Takezoe, Wenze Hu, Manmohan Chandraker, Li-Jia Li, Vijay K. Narayanan, Xiaoyu Wang
To facilitate the research in this field, this paper contributes an active learning benchmark framework named as ALBench for evaluating active learning in object detection.
1 code implementation • 25 Jul 2022 • Tarun Kalluri, Astuti Sharma, Manmohan Chandraker
Practical real world datasets with plentiful categories introduce new challenges for unsupervised domain adaptation like small inter-class discriminability, that existing approaches relying on domain invariance alone cannot handle sufficiently well.
Fine-Grained Visual Recognition Unsupervised Domain Adaptation
1 code implementation • 18 Jul 2022 • Shiyu Zhao, Zhixing Zhang, Samuel Schulter, Long Zhao, Vijay Kumar B. G, Anastasis Stathopoulos, Manmohan Chandraker, Dimitris Metaxas
We propose a novel method that leverages the rich semantics available in recent vision and language models to localize and classify objects in unlabeled images, effectively generating pseudo labels for object detection.
Ranked #15 on Open Vocabulary Object Detection on MSCOCO (using extra training data)
1 code implementation • CVPR 2022 • Yu-Ying Yeh, Zhengqin Li, Yannick Hold-Geoffroy, Rui Zhu, Zexiang Xu, Miloš Hašan, Kalyan Sunkavalli, Manmohan Chandraker
Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout.
no code implementations • CVPR 2022 • Rui Zhu, Zhengqin Li, Janarbek Matai, Fatih Porikli, Manmohan Chandraker
Indoor scenes exhibit significant appearance variations due to myriad interactions between arbitrarily diverse object shapes, spatially-changing materials, and complex lighting.
no code implementations • 19 May 2022 • Zhengqin Li, Jia Shi, Sai Bi, Rui Zhu, Kalyan Sunkavalli, Miloš Hašan, Zexiang Xu, Ravi Ramamoorthi, Manmohan Chandraker
We tackle this problem using two novel components: 1) a holistic scene reconstruction method that estimates scene reflectance and parametric 3D lighting, and 2) a neural rendering framework that re-renders the scene from our predictions.
no code implementations • 14 Apr 2022 • Ishit Mehta, Manmohan Chandraker, Ravi Ramamoorthi
Our method uses the flow field to deform parametric implicit surfaces by extending the classical theory of level sets.
no code implementations • CVPR 2022 • Dripta S. Raychaudhuri, Yumin Suh, Samuel Schulter, Xiang Yu, Masoud Faraki, Amit K. Roy-Chowdhury, Manmohan Chandraker
In contrast to the existing dynamic multi-task approaches that adjust only the weights within a fixed architecture, our approach affords the flexibility to dynamically control the total computational cost and match the user-preferred task importance better.
1 code implementation • 27 Mar 2022 • Zaid Khan, Vijay Kumar BG, Xiang Yu, Samuel Schulter, Manmohan Chandraker, Yun Fu
Self-supervised vision-language pretraining from pure images and text with a contrastive loss is effective, but ignores fine-grained alignment due to a dual-stream architecture that aligns image and text representations only on a global level.
no code implementations • CVPR 2022 • Christian Simon, Masoud Faraki, Yi-Hsuan Tsai, Xiang Yu, Samuel Schulter, Yumin Suh, Mehrtash Harandi, Manmohan Chandraker
Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task.
no code implementations • 28 Feb 2022 • Dongwan Kim, Yi-Hsuan Tsai, Yumin Suh, Masoud Faraki, Sparsh Garg, Manmohan Chandraker, Bohyung Han
First, a gradient conflict in training due to mismatched label spaces is identified and a class-independent binary cross-entropy loss is proposed to alleviate such label conflicts.
1 code implementation • 19 Nov 2021 • Phoenix X. Huang, Wenze Hu, William Brendel, Manmohan Chandraker, Li-Jia Li, Xiaoyu Wang
This paper introduces an open source platform to support the rapid development of computer vision applications at scale.
no code implementations • CVPR 2022 • Chang Liu, Xiang Yu, Yi-Hsuan Tsai, Ramin Moslemi, Masoud Faraki, Manmohan Chandraker, Yun Fu
Convolutional Neural Networks have achieved remarkable success in face recognition, in part due to the abundant availability of data.
no code implementations • ICCV 2021 • Donghyun Kim, Yi-Hsuan Tsai, Bingbing Zhuang, Xiang Yu, Stan Sclaroff, Kate Saenko, Manmohan Chandraker
Learning transferable and domain adaptive feature representations from videos is important for video-relevant tasks such as action recognition.
no code implementations • CVPR 2021 • Zhengqin Li, Ting-Wei Yu, Shen Sang, Sarah Wang, Meng Song, YuHan Liu, Yu-Ying Yeh, Rui Zhu, Nitesh Gundavarapu, Jia Shi, Sai Bi, Hong-Xing Yu, Zexiang Xu, Kalyan Sunkavalli, Milos Hasan, Ravi Ramamoorthi, Manmohan Chandraker
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
no code implementations • CVPR 2021 • Sriram Narayanan, Ramin Moslemi, Francesco Pittaluga, Buyu Liu, Manmohan Chandraker
Our second contribution is a novel trajectory prediction framework called ALAN that uses existing lane centerlines as anchors to provide trajectories constrained to the input lanes.
no code implementations • CVPR 2021 • Bingbing Zhuang, Manmohan Chandraker
While we focus on relative pose, we envision that our pipeline is broadly applicable for fusing classical geometry and deep learning.
no code implementations • CVPR 2022 • Buyu Liu, Bingbing Zhuang, Manmohan Chandraker
We propose an end-to-end network that takes a single perspective RGB image of a complex road scene as input, to produce occlusion-reasoned layouts in perspective space as well as a parametric bird's-eye-view (BEV) space.
2 code implementations • ICCV 2021 • Ishit Mehta, Michaël Gharbi, Connelly Barnes, Eli Shechtman, Ravi Ramamoorthi, Manmohan Chandraker
Our approach produces generalizable functional representations of images, videos and shapes, and achieves higher reconstruction quality than prior works that are optimized for a single signal.
1 code implementation • CVPR 2021 • Astuti Sharma, Tarun Kalluri, Manmohan Chandraker
Domain adaptation deals with training models using large scale labeled data from a specific source domain and then adapting the knowledge to certain target domains that have few or no labels.
no code implementations • CVPR 2021 • Masoud Faraki, Xiang Yu, Yi-Hsuan Tsai, Yumin Suh, Manmohan Chandraker
Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains.
1 code implementation • 15 Dec 2020 • Tarun Kalluri, Deepak Pathak, Manmohan Chandraker, Du Tran
A majority of methods for video frame interpolation compute bidirectional optical flow between adjacent frames of a video, followed by a suitable warping algorithm to generate the output frames.
Ranked #2 on Video Frame Interpolation on GoPro
no code implementations • 28 Nov 2020 • Junru Wu, Xiang Yu, Buyu Liu, Zhangyang Wang, Manmohan Chandraker
Face anti-spoofing (FAS) seeks to discriminate genuine faces from fake ones arising from any type of spoofing attack.
no code implementations • 9 Oct 2020 • Yuqing Zhu, Xiang Yu, Yi-Hsuan Tsai, Francesco Pittaluga, Masoud Faraki, Manmohan Chandraker, Yu-Xiang Wang
Differentially Private Federated Learning (DPFL) is an emerging field with many applications.
no code implementations • ECCV 2020 • Xiangyun Zhao, Samuel Schulter, Gaurav Sharma, Yi-Hsuan Tsai, Manmohan Chandraker, Ying Wu
To address this challenge, we design a framework which works with such partial annotations, and we exploit a pseudo labeling approach that we adapt for our specific case.
no code implementations • ECCV 2020 • Sujoy Paul, Yi-Hsuan Tsai, Samuel Schulter, Amit K. Roy-Chowdhury, Manmohan Chandraker
In this work, we propose a novel framework for domain adaptation in semantic segmentation with image-level weak labels in the target domain.
1 code implementation • 29 Jul 2020 • You-Yi Jau, Rui Zhu, Hao Su, Manmohan Chandraker
Estimating relative camera poses from consecutive frames is a fundamental problem in visual odometry (VO) and simultaneous localization and mapping (SLAM), where classic methods consisting of hand-crafted features and sampling-based outlier rejection have been a dominant choice for over a decade.
no code implementations • ECCV 2020 • Sriram N. N, Buyu Liu, Francesco Pittaluga, Manmohan Chandraker
Our second contribution is a novel method that generates diverse predictions while accounting for scene semantics and multi-agent interactions, with constant-time inference independent of the number of agents.
no code implementations • 25 Jul 2020 • Zhengqin Li, Ting-Wei Yu, Shen Sang, Sarah Wang, Meng Song, YuHan Liu, Yu-Ying Yeh, Rui Zhu, Nitesh Gundavarapu, Jia Shi, Sai Bi, Zexiang Xu, Hong-Xing Yu, Kalyan Sunkavalli, Miloš Hašan, Ravi Ramamoorthi, Manmohan Chandraker
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
1 code implementation • NeurIPS 2020 • Kunal Gupta, Manmohan Chandraker
Applications like rendering, simulations and 3D printing require meshes to be manifold so that they can interact with the world like the real objects they represent.
no code implementations • ECCV 2020 • Yuliang Zou, Pan Ji, Quoc-Huy Tran, Jia-Bin Huang, Manmohan Chandraker
Monocular visual odometry (VO) suffers severely from error accumulation during frame-to-frame pose estimation.
1 code implementation • ECCV 2020 • Rui Zhu, Xingyi Yang, Yannick Hold-Geoffroy, Federico Perazzi, Jonathan Eisenmann, Kalyan Sunkavalli, Manmohan Chandraker
Most 3D reconstruction methods may only recover scene properties up to a global scale ambiguity.
no code implementations • ECCV 2020 • Aruni RoyChowdhury, Xiang Yu, Kihyuk Sohn, Erik Learned-Miller, Manmohan Chandraker
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reducing the cost of human annotation.
no code implementations • CVPR 2020 • Buyu Liu, Bingbing Zhuang, Samuel Schulter, Pan Ji, Manmohan Chandraker
(2) Introducing the LSTM and FTM modules improves the prediction consistency in videos.
1 code implementation • ECCV 2020 • Lokender Tiwari, Pan Ji, Quoc-Huy Tran, Bingbing Zhuang, Saket Anand, Manmohan Chandraker
Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely disjoint approaches towards building a 3D map of the surrounding environment.
1 code implementation • CVPR 2020 • Zhengqin Li, Yu-Ying Yeh, Manmohan Chandraker
Recovering the 3D shape of transparent objects using a small number of unconstrained natural images is an ill-posed problem.
no code implementations • CVPR 2020 • Yichun Shi, Xiang Yu, Kihyuk Sohn, Manmohan Chandraker, Anil K. Jain
Recognizing wild faces is extremely hard as they appear with all kinds of variations.
no code implementations • 7 Dec 2019 • Junru Wu, Xiang Yu, Ding Liu, Manmohan Chandraker, Zhangyang Wang
To train and evaluate on more diverse blur severity levels, we propose a Challenging DVD dataset generated from the raw DVD video set by pooling frames with different temporal windows.
no code implementations • 22 Nov 2019 • Taihong Xiao, Yi-Hsuan Tsai, Kihyuk Sohn, Manmohan Chandraker, Ming-Hsuan Yang
For instance, there could be a potential privacy risk of machine learning systems via the model inversion attack, whose goal is to reconstruct the input data from the latent representation of deep networks.
no code implementations • 30 Jul 2019 • Bingbing Zhuang, Quoc-Huy Tran, Pan Ji, Gim Hee Lee, Loong Fah Cheong, Manmohan Chandraker
Self-calibration of camera intrinsics and radial distortion has a long history of research in the computer vision community.
no code implementations • 24 Jul 2019 • Feng-Ju Chang, Xiang Yu, Ram Nevatia, Manmohan Chandraker
We address the challenging problem of generating facial attributes using a single image in an unconstrained pose.
no code implementations • 5 Jun 2019 • Shuyang Dai, Kihyuk Sohn, Yi-Hsuan Tsai, Lawrence Carin, Manmohan Chandraker
We tackle an unsupervised domain adaptation problem for which the domain discrepancy between labeled source and unlabeled target domains is large, due to many factors of inter and intra-domain variation.
1 code implementation • CVPR 2020 • Zhengqin Li, Mohammad Shafiei, Ravi Ramamoorthi, Kalyan Sunkavalli, Manmohan Chandraker
Our inverse rendering network incorporates physical insights -- including a spatially-varying spherical Gaussian lighting representation, a differentiable rendering layer to model scene appearance, a cascade structure to iteratively refine the predictions and a bilateral solver for refinement -- allowing us to jointly reason about shape, lighting, and reflectance.
no code implementations • ICLR 2019 • Yi-Hsuan Tsai, Kihyuk Sohn, Samuel Schulter, Manmohan Chandraker
To this end, we propose to learn discriminative feature representations of patches based on label histograms in the source domain, through the construction of a disentangled space.
no code implementations • ICLR 2019 • Kihyuk Sohn, Wenling Shang, Xiang Yu, Manmohan Chandraker
Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from a source domain.
no code implementations • 16 Apr 2019 • Jong-Chyi Su, Yi-Hsuan Tsai, Kihyuk Sohn, Buyu Liu, Subhransu Maji, Manmohan Chandraker
Our approach, active adversarial domain adaptation (AADA), explores a duality between two related problems: adversarial domain alignment and importance sampling for adapting models across domains.
8 code implementations • ICCV 2019 • Yi-Hsuan Tsai, Kihyuk Sohn, Samuel Schulter, Manmohan Chandraker
Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks.
Ranked #22 on Image-to-Image Translation on SYNTHIA-to-Cityscapes
no code implementations • CVPR 2019 • Ziyan Wang, Buyu Liu, Samuel Schulter, Manmohan Chandraker
In this paper, we address the problem of inferring the layout of complex road scenes given a single camera as input.
2 code implementations • 26 Nov 2018 • Girish Varma, Anbumani Subramanian, Anoop Namboodiri, Manmohan Chandraker, C. V. Jawahar
It also reflects label distributions of road scenes significantly different from existing datasets, with most classes displaying greater within-class diversity.
1 code implementation • ICCV 2019 • Tarun Kalluri, Girish Varma, Manmohan Chandraker, C. V. Jawahar
In recent years, the need for semantic segmentation has arisen across several different applications and environments.
Ranked #27 on Semantic Segmentation on DensePASS (using extra training data)
no code implementations • ICLR 2019 • Nataniel Ruiz, Samuel Schulter, Manmohan Chandraker
Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire.
no code implementations • ECCV 2018 • Zhengqin Li, Kalyan Sunkavalli, Manmohan Chandraker
We propose a material acquisition approach to recover the spatially-varying BRDF and normal map of a near-planar surface from a single image captured by a handheld mobile phone camera.
no code implementations • 28 Mar 2018 • Tuan-Hung Vu, Wongun Choi, Samuel Schulter, Manmohan Chandraker
This paper proposes a novel memory-based online video representation that is efficient, accurate and predictive.
no code implementations • ECCV 2018 • Samuel Schulter, Menghua Zhai, Nathan Jacobs, Manmohan Chandraker
Given a single RGB image of a complex outdoor road scene in the perspective view, we address the novel problem of estimating an occlusion-reasoned semantic scene layout in the top-view.
no code implementations • 23 Mar 2018 • Xi Yin, Xiang Yu, Kihyuk Sohn, Xiaoming Liu, Manmohan Chandraker
In this paper, we propose a center-based feature transfer framework to augment the feature space of under-represented subjects from the regular subjects that have sufficiently diverse samples.
no code implementations • ECCV 2018 • Mohammed E. Fathy, Quoc-Huy Tran, M. Zeeshan Zia, Paul Vernaza, Manmohan Chandraker
Further, we propose to use activation maps at different layers of a CNN, as an effective and principled replacement for the multi-resolution image pyramids often used for matching tasks.
1 code implementation • CVPR 2019 • Luan Tran, Kihyuk Sohn, Xiang Yu, Xiaoming Liu, Manmohan Chandraker
Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or input pixels.
12 code implementations • CVPR 2018 • Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, Manmohan Chandraker
In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation.
Ranked #3 on Domain Adaptation on Synscapes-to-Cityscapes
no code implementations • CVPR 2017 • Paul Vernaza, Manmohan Chandraker
Large-scale training for semantic segmentation is challenging due to the expense of obtaining training data for this task relative to other vision tasks.
no code implementations • 8 Jan 2018 • Chi Li, M. Zeeshan Zia, Quoc-Huy Tran, Xiang Yu, Gregory D. Hager, Manmohan Chandraker
In this work, we explore an approach for injecting prior domain structure into neural network training by supervising hidden layers of a CNN with intermediate concepts that normally are not observed in practice.
no code implementations • NeurIPS 2017 • Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, Manmohan Chandraker
In this work, we propose a new framework to learn compact and fast ob- ject detection networks with improved accuracy using knowledge distillation [20] and hint learning [34].
no code implementations • ICCV 2017 • Kihyuk Sohn, Sifei Liu, Guangyu Zhong, Xiang Yu, Ming-Hsuan Yang, Manmohan Chandraker
Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of curating diverse large-scale video datasets.
no code implementations • CVPR 2017 • Zhengqin Li, Zexiang Xu, Ravi Ramamoorthi, Manmohan Chandraker
On the other hand, recent works have explored PDE invariants for shape recovery with complex BRDFs, but they have not been incorporated into robust numerical optimization frameworks.
no code implementations • CVPR 2017 • Samuel Schulter, Paul Vernaza, Wongun Choi, Manmohan Chandraker
In this work, we demonstrate that it is possible to learn features for network-flow-based data association via backpropagation, by expressing the optimum of a smoothed network flow problem as a differentiable function of the pairwise association costs.
2 code implementations • 31 May 2017 • JunYoung Gwak, Christopher B. Choy, Animesh Garg, Manmohan Chandraker, Silvio Savarese
Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks.
no code implementations • ICCV 2017 • Xi Yin, Xiang Yu, Kihyuk Sohn, Xiaoming Liu, Manmohan Chandraker
Despite recent advances in face recognition using deep learning, severe accuracy drops are observed for large pose variations in unconstrained environments.
3 code implementations • CVPR 2017 • Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B. Choy, Philip H. S. Torr, Manmohan Chandraker
DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i. e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents.
Ranked #1 on Trajectory Prediction on PAID
no code implementations • ICCV 2017 • Xi Peng, Xiang Yu, Kihyuk Sohn, Dimitris Metaxas, Manmohan Chandraker
Finally, we propose a new feature reconstruction metric learning to explicitly disentangle identity and pose, by demanding alignment between the feature reconstructions through various combinations of identity and pose features, which is obtained from two images of the same subject.
no code implementations • CVPR 2017 • Chi Li, M. Zeeshan Zia, Quoc-Huy Tran, Xiang Yu, Gregory D. Hager, Manmohan Chandraker
Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation.
no code implementations • 24 Aug 2016 • Ting-Chun Wang, Jun-Yan Zhu, Ebi Hiroaki, Manmohan Chandraker, Alexei A. Efros, Ravi Ramamoorthi
We introduce a new light-field dataset of materials, and take advantage of the recent success of deep learning to perform material recognition on the 4D light-field.
no code implementations • NeurIPS 2016 • Christopher B. Choy, JunYoung Gwak, Silvio Savarese, Manmohan Chandraker
We present a deep learning framework for accurate visual correspondences and demonstrate its effectiveness for both geometric and semantic matching, spanning across rigid motions to intra-class shape or appearance variations.
no code implementations • CVPR 2016 • Ting-Chun Wang, Manmohan Chandraker, Alexei A. Efros, Ravi Ramamoorthi
Light-field cameras have recently emerged as a powerful tool for one-shot passive 3D shape capture.
no code implementations • CVPR 2016 • Vikas Dhiman, Quoc-Huy Tran, Jason J. Corso, Manmohan Chandraker
We present a physically interpretable, continuous 3D model for handling occlusions with applications to road scene understanding.
no code implementations • 3 May 2016 • Xiang Yu, Feng Zhou, Manmohan Chandraker
We propose a novel cascaded framework, namely deep deformation network (DDN), for localizing landmarks in non-rigid objects.
no code implementations • CVPR 2016 • Angjoo Kanazawa, David W. Jacobs, Manmohan Chandraker
This is in contrast to prior works that require part annotations, since matching objects across class and pose variations is challenging with appearance features alone.
no code implementations • CVPR 2017 • Liang Zheng, Hengheng Zhang, Shaoyan Sun, Manmohan Chandraker, Yi Yang, Qi Tian
Our baselines address three issues: the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification accuracy and assessing the effectiveness of different detectors for re-identification.
no code implementations • CVPR 2015 • Shiyu Song, Manmohan Chandraker
Experiments on the KITTI dataset show the efficacy of our cues, as well as the accuracy and robustness of our 3D object localization relative to ground truth and prior works.
no code implementations • CVPR 2014 • Shiyu Song, Manmohan Chandraker
Experiments on the KITTI dataset demonstrate the accuracy of our ground plane estimation, monocular SFM and object localization relative to ground truth, with detailed comparisons to prior art.
no code implementations • CVPR 2014 • Manmohan Chandraker
For the perspective case, we show that three differential motions suffice to yield surface depth for unknown isotropic BRDF and unknown directional lighting, while additional constraints are obtained with restrictions on BRDF or lighting.
no code implementations • CVPR 2013 • Sid Yingze Bao, Manmohan Chandraker, Yuanqing Lin, Silvio Savarese
Given multiple images of an unseen instance, we collate information from 2D object detectors to align the structure from motion point cloud with the mean shape, which is subsequently warped and refined to approach the actual shape.
no code implementations • CVPR 2013 • Manmohan Chandraker, Dikpal Reddy, Yizhou Wang, Ravi Ramamoorthi
Under orthographic projection, we prove that three differential motions suffice to yield an invariant that relates shape to image derivatives, regardless of BRDF and illumination.